A comprehensive and precise analysis of shale gas production performance is crucial for evaluating resource potential,designing a field development plan,and making investment decisions.However,quantitative analysis ca...A comprehensive and precise analysis of shale gas production performance is crucial for evaluating resource potential,designing a field development plan,and making investment decisions.However,quantitative analysis can be challenging because production performance is dominated by the complex interaction among a series of geological and engineering factors.In fact,each factor can be viewed as a player who makes cooperative contributions to the production payoff within the constraints of physical laws and models.Inspired by the idea,we propose a hybrid data-driven analysis framework in this study,where the contributions of dominant factors are quantitatively evaluated,the productions are precisely forecasted,and the development optimization suggestions are comprehensively generated.More specifically,game theory and machine learning models are coupled to determine the dominating geological and engineering factors.The Shapley value with definite physical meaning is employed to quantitatively measure the effects of individual factors.A multi-model-fused stacked model is trained for production forecast,which provides the basis for derivative-free optimization algorithms to optimize the development plan.The complete workflow is validated with actual production data collected from the Fuling shale gas field,Sichuan Basin,China.The validation results show that the proposed procedure can draw rigorous conclusions with quantified evidence and thereby provide specific and reliable suggestions for development plan optimization.Comparing with traditional and experience-based approaches,the hybrid data-driven procedure is advanced in terms of both efficiency and accuracy.展开更多
Economics is a science that studies how the economy grows,so the theory of economic growth is the most important theory of economics.In the real market economy society,people achieve the goal of economic growth throug...Economics is a science that studies how the economy grows,so the theory of economic growth is the most important theory of economics.In the real market economy society,people achieve the goal of economic growth through two kinds of economic activities:production and transaction.Then a correct economic growth theory must be one that can explain both production and transaction economic activities.Just like Newton’s law of universal gravitation in physics,it can explain the motion law of all objects.For a long time,we have been dominated by the western economic growth theory of western mainstream economics.It is not difficult to find that it has a fatal defect,which can only explain production economic activities but not transaction economic activities.So it can’t explain the Chinese economy,and it can’t explain the western economy.The new economic growth theory proposed in this paper makes up for the defects of western economic growth theory,and it is the terminator of western economic growth theory.This is a revolution of new economics to traditional western economics.展开更多
In this paper,we assess the role of investment in research and development(R&D)and economic policy uncertainty(EPU)in Sri Lanka’s economic growth experience.We do this by first determining which endogenous growth...In this paper,we assess the role of investment in research and development(R&D)and economic policy uncertainty(EPU)in Sri Lanka’s economic growth experience.We do this by first determining which endogenous growth theories best explain the evolution of total factor productivity(TFP)in the country.Using historical time series data(1980–2018),we find that semi-endogenous growth theories best explain the evolution of TFP in Sri Lanka.This evidence suggests that R&D is critical to the country’s TFP expansion.We find that,through R&D,EPU has a crucial detrimental impact on TFP growth,although it is short-lived.Our findings are robust and have important implications for R&D investment and for moderating EPU.展开更多
Extreme events are defined as values of the event below or above a certain value called threshold. A well chosen threshold helps to identify the extreme levels. Several methods have been used to determine threshold so...Extreme events are defined as values of the event below or above a certain value called threshold. A well chosen threshold helps to identify the extreme levels. Several methods have been used to determine threshold so as to analyze and model extreme events. One of the most successful methods is the maximum product of spacing (MPS). However, there is a problem encountered while modeling data through this method in that the method breaks down when there is a tie in the exceedances. This study offers a solution to model data even if it contains ties. To do so, an optimal threshold that gives more optimal parameters for extreme events, was determined. The study achieved its main objective by deriving a method that improved MPS method for determining an optimal threshold for extreme values in a data set containing ties, estimated the Generalized Pareto Distribution (GPD) parameters for the optimal threshold derived and compared these GPD parameters with GPD parameters determined through the standard MPS model. The study improved maximum product of spacing method and used Generalized Pareto Distribution (GPD) and Peak over threshold (POT) methods as the basis of identifying extreme values. This study will help the statisticians in different sectors of our economy to model extreme events involving ties. To statisticians, the structure of the extreme levels which exist in the tails of the ordinary distributions is very important in analyzing, predicting and forecasting the likelihood of an occurrence of the extreme event.展开更多
基金This work was supported by the National Natural Science Foundation of China(Grant No.42050104)the Science Foundation of SINOPEC Group(Grant No.P20030).
文摘A comprehensive and precise analysis of shale gas production performance is crucial for evaluating resource potential,designing a field development plan,and making investment decisions.However,quantitative analysis can be challenging because production performance is dominated by the complex interaction among a series of geological and engineering factors.In fact,each factor can be viewed as a player who makes cooperative contributions to the production payoff within the constraints of physical laws and models.Inspired by the idea,we propose a hybrid data-driven analysis framework in this study,where the contributions of dominant factors are quantitatively evaluated,the productions are precisely forecasted,and the development optimization suggestions are comprehensively generated.More specifically,game theory and machine learning models are coupled to determine the dominating geological and engineering factors.The Shapley value with definite physical meaning is employed to quantitatively measure the effects of individual factors.A multi-model-fused stacked model is trained for production forecast,which provides the basis for derivative-free optimization algorithms to optimize the development plan.The complete workflow is validated with actual production data collected from the Fuling shale gas field,Sichuan Basin,China.The validation results show that the proposed procedure can draw rigorous conclusions with quantified evidence and thereby provide specific and reliable suggestions for development plan optimization.Comparing with traditional and experience-based approaches,the hybrid data-driven procedure is advanced in terms of both efficiency and accuracy.
文摘Economics is a science that studies how the economy grows,so the theory of economic growth is the most important theory of economics.In the real market economy society,people achieve the goal of economic growth through two kinds of economic activities:production and transaction.Then a correct economic growth theory must be one that can explain both production and transaction economic activities.Just like Newton’s law of universal gravitation in physics,it can explain the motion law of all objects.For a long time,we have been dominated by the western economic growth theory of western mainstream economics.It is not difficult to find that it has a fatal defect,which can only explain production economic activities but not transaction economic activities.So it can’t explain the Chinese economy,and it can’t explain the western economy.The new economic growth theory proposed in this paper makes up for the defects of western economic growth theory,and it is the terminator of western economic growth theory.This is a revolution of new economics to traditional western economics.
文摘In this paper,we assess the role of investment in research and development(R&D)and economic policy uncertainty(EPU)in Sri Lanka’s economic growth experience.We do this by first determining which endogenous growth theories best explain the evolution of total factor productivity(TFP)in the country.Using historical time series data(1980–2018),we find that semi-endogenous growth theories best explain the evolution of TFP in Sri Lanka.This evidence suggests that R&D is critical to the country’s TFP expansion.We find that,through R&D,EPU has a crucial detrimental impact on TFP growth,although it is short-lived.Our findings are robust and have important implications for R&D investment and for moderating EPU.
文摘Extreme events are defined as values of the event below or above a certain value called threshold. A well chosen threshold helps to identify the extreme levels. Several methods have been used to determine threshold so as to analyze and model extreme events. One of the most successful methods is the maximum product of spacing (MPS). However, there is a problem encountered while modeling data through this method in that the method breaks down when there is a tie in the exceedances. This study offers a solution to model data even if it contains ties. To do so, an optimal threshold that gives more optimal parameters for extreme events, was determined. The study achieved its main objective by deriving a method that improved MPS method for determining an optimal threshold for extreme values in a data set containing ties, estimated the Generalized Pareto Distribution (GPD) parameters for the optimal threshold derived and compared these GPD parameters with GPD parameters determined through the standard MPS model. The study improved maximum product of spacing method and used Generalized Pareto Distribution (GPD) and Peak over threshold (POT) methods as the basis of identifying extreme values. This study will help the statisticians in different sectors of our economy to model extreme events involving ties. To statisticians, the structure of the extreme levels which exist in the tails of the ordinary distributions is very important in analyzing, predicting and forecasting the likelihood of an occurrence of the extreme event.
